The normal definition of the word "thinking" definitely includes what LLMs do. Hell people used to say computers were thinking even before AI. It's super weird to get all uppity about the semantics of the word now.
If a model isn’t able to count to 100, but is able to use python to do math, it isn’t thinking.
If you are only an outcomes person, then this difference doesn’t matter to you.
But when people are talking about AGI, anthropomorphizing LLMs, and making assumptions on its behavior based on that, then people are being loose or misled.
Language changes over time anyway, so even if you really believe what LLMs are doing isn't the "thinking" of 2024, it probably will be the "thinking" of 2027, because most people are using it that way.
And yes, we do need precision. Language is a vehicle to convey ideas, at its best it creates clarity. At its worst it creates deception and confusion.
What the labs are doing is designed to mislead. Being clear about the terms and avoiding needless anthropomorphization, is a basic decency we owe to the many others who are being riled up or terrified by the conclusions and implications that come from using “thinking” when it’s something else.
The difference between being satisfied by the outcomes and being interested in the process is sufficient to indicate the exactness required in your terminology, and does not harm any position.
These aren’t thinking machines, but if what they simulate is good enough, and your work ends with the output, it doesn’t quite matter.
People can be happy with a meal from McDonalds, or they can want something prepared by a chef, or Soylent.
All are acceptable and bring utility to their customers.
The models are simulating thinking, if the fidelity is good enough for you - great!